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The advancement of hydrogen technology and rising environmental concerns have shifted research toward renewable energy for green hydrogen production. This study introduces a novel tri-level transaction methodology for microgrid clusters, addressing uncertainties and price fluctuations in hydrogen. We establish a comprehensive microgrid topology with distributed power generation and hydrogen production facilities. A polygonal uncertainty set method quantifies wind and solar energy uncertainties, while an enhanced interval optimization technique refines the model. We integrate a sophisticated demand response model for hydrogen loading, capturing users' behavior in response to price changes, thereby improving renewable energy utilization and supporting economically viable management practices. Additionally, we propose a tri-level game-theoretic framework for analyzing stakeholder interactions in microgrid clusters, incorporating supply–demand dynamics and a master–slave structure for microgrids and users. A distributed algorithm, "KKT & supply-demand ratio", solves large-scale optimization problems by integrating Karush–Kuhn–Tucker conditions with a heuristic approach. Our simulations validate the methodology, demonstrating that accounting for uncertainties and dynamic hydrogen prices enhances renewable energy use and economic efficiency, optimizing social welfare for operators and economic benefits for microgrids and users. [ABSTRACT FROM AUTHOR]

The power-to-X strategy for passenger car applications offers a viable solution for using the surplus electrical power from renewable energy sources instead of exporting it to the grid. The innovative system proposed in this study allocates surplus electrical power from a building-integrated biomass-based Combined Cooling Heating and Power (CCHP) system to on-site applications and evaluates the energetic and economic benefits. The system comprises two key components: a 50 kW electric vehicle (EV) charging station for EVs and a 50 kW alkaline electrolyzer system for on-site hydrogen production, which is later dispensed to fuel cell electric vehicles (FCEVs). The primary goal is to decrease the surplus of electricity exports while simultaneously encouraging sustainable transportation. The system's economic viability is assessed through two scenarios of fuel (e.g., biomass) supply costs (e.g., with and without fuel market costs) and compared to the conventional approach of exporting the excess power. The key findings of this work include a substantial reduction in surplus electricity exports, with only 3.7% allocated for EV charging and 31.5% for hydrogen production. The simple payback period (SPB) is notably reduced, enhancing economic viability. Sensitivity analysis identifies the optimal hydrogen system, featuring a 120 kW electrolyzer and a 37 kg daily hydrogen demand. The results underscore the importance of prioritizing self-consumed energy over exports to the national grid, thereby supporting integrated renewable energy solutions that enhance local energy utilization and promote sustainable transportation initiatives. [ABSTRACT FROM AUTHOR]

The modular multilevel converter (MMC) plays an important role in large-scale renewable energy integration and transmission, and it can also operate in standalone mode, powering AC passive loads. This paper focuses on the impact of load variation on the stability of the MMC. First, the impact of load variation on the MMC transfer function is analyzed in detail using the harmonic state-space (HSS) modeling method. Then, by means of the impedance-based stability analysis method, it is found that the MMC tends to become unstable with the increase in inductive loads. If the controller is not well-designed, the system may fail when loads change. Therefore, the worst-case design is used to guarantee the overall system's stability under all load conditions. The impact of traditional proportional resonant (PR) controller parameters on the system's stability is analyzed, revealing that the stability margin and control performance of the controller are limited. Thus, an improved controller structure with an additional series of compensators is proposed. Extensive simulation results in MATLAB/Simulink R2024a verify the analysis of this work and the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Reducing maritime greenhouse gas (GHG) emissions is challenging. As efforts to address climate change are gaining momentum, reducing the environmental impact becomes crucial for maritime short-to-medium-term sustainability. The International Maritime Organisation (IMO) has adopted Life Cycle Assessment (LCA) guidelines for estimating GHG emissions associated with alternative fuels. This paper proposes an examination of the latest IMO-adopted LCA guidelines, comparing them with existing methodologies used for the transport sector. By scrutinising these guidelines, the paper aims to provide a better understanding of the evolving landscape for GHG emission estimation within the maritime sector. The paper presents a case study that applies the newly established LCA guidelines to a promising alternative fuel pathway, i.e., waste-wood-derived pyrolysis oil. Pyrolysis oil offers an attractive option, leveraging waste materials to generate a sustainable energy source. The environmental impact of pyrolysis oils is quantified according to the IMO LCA guidelines, offering insights into its viability as a cleaner alternative as marine fuel. The results show the large potential for GHG savings offered by this pathway: upgraded pyrolysis oil can deliver significant GHG savings, and this contribution is linearly dependent of its energy share when blended with standard Heavy Fuel Oil. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition. [ABSTRACT FROM AUTHOR]

In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation of bifacial module efficiency compared to traditional monofacial modules. The experimental setup simulates diverse installation environments, including rooftops and ground-mounted systems, by varying background reflectance. Key performance metrics such as energy yield, exergy yield, and overall efficiency were measured. The findings reveal that bifacial modules installed over white gravel backgrounds achieve the highest exergy profile and efficiency during peak solar radiation periods, attributed to the enhanced reflectivity of white gravel. These insights can inform strategic decisions regarding the selection and placement of bifacial modules to optimize energy and exergy outputs in real-world scenarios. This study contributes valuable knowledge to the advancement of renewable energy technologies, offering guidance for researchers, developers, and policymakers focused on sustainable energy solutions. [ABSTRACT FROM AUTHOR]

In 2020, the transportation sector was the second largest source of carbon emissions in the UK and in Newcastle upon Tyne, responsible for about 33% of total emissions. To support the UK's target of reaching net zero emissions by 2050, electric vehicles (EVs) are pivotal in advancing carbon-neutral road transportation. Optimal EV charging requires a better understanding of the unpredictable output from on-site renewable energy sources (ORES). This paper proposes an integrated EV fleet charging schedule with a proximal policy optimization method based on a framework for deep reinforcement learning. For the design of the reinforcement learning environment, mathematical models of wind and solar power generation are created. In addition, the multivariate Gaussian distributions derived from historical weather and EV fleet charging data are utilized to simulate weather and charging demand uncertainty in order to create large datasets for training the model. The optimization problem is expressed as a Markov decision process (MDP) with operational constraints. For training artificial neural networks (ANNs) through successive transition simulations, a proximal policy optimization (PPO) approach is devised. The optimization approach is deployed and evaluated on a real-world scenario comprised of council EV fleet charging data from Leicester, UK. The results show that due to the design of the rewards function and system limitations, the charging action is biased towards the time of day when renewable energy output is maximum (midday). The charging decision by reinforcement learning improves the utilization of renewable energy by 2–4% compared to the random charging policy and the priority charging policy. This study contributes to the reduction in battery charging and discharging, electricity sold to the grid to create benefits and the reduction in carbon emissions. [ABSTRACT FROM AUTHOR]

The document discusses the application of optimization principles in the planning and operation of active distribution networks (ADNs) to handle distributed generation resources like renewables, battery energy storage systems (BESSs), and electric vehicles (EVs). Various optimization techniques are used to address challenges such as voltage regulation, loss minimization, demand response, and renewable energy integration, aiming to develop efficient, flexible, and sustainable networks transitioning towards smart grids. The document also highlights the importance of multi-objective optimization approaches in balancing conflicting objectives like cost, reliability, and sustainability, making objective functions essential tools for modern energy distribution systems. [Extracted from the article]

With the proposed "double carbon" target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. [ABSTRACT FROM AUTHOR]

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system's balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance. [ABSTRACT FROM AUTHOR]

This study examines the optimal energy mix for industrial complexes by incorporating renewable energy systems, decarbonization strategies, and sector coupling technologies. Using data from the Balan Industrial Complex in Korea, five energy scenarios were evaluated, ranging from conventional systems (Scenario 1) to advanced renewable configurations (Scenario 5). The results show that Scenario 5, which integrates sector coupling systems and decarbonization technologies, is the most cost-effective and environmentally sustainable. Scenario 5 achieves the lowest Net Present Cost (NPC), and significantly reduces CO2 emissions. Furthermore, an analysis of electricity prices and CO2 costs from Korea, the United States, and Germany highlights the critical role of regional electricity tariffs and carbon pricing in determining the economic feasibility of energy systems. While renewable setups require higher initial investments, Scenario 5 proves to be the most economically viable over time, offering both cost savings and environmental benefits. These findings provide valuable insights for policymakers and industry leaders, emphasizing the importance of customized strategies to optimize energy systems in industrial applications. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to present family farms as consumers and producers of renewable energies which provide them with an opportunity to reduce operating costs. The time scope of the study is 2014–2022, and the Farm Accountancy Data Network is used as the data source. The following research methods were employed: comparative and descriptive analysis, intensity indicators, ranking assignment and panel regression. Based on the values of energy output and energy costs, the rankings revealed a strong position of the Netherlands and Germany. As demonstrated by the study, energy production and consumption volumes depend on the farms' economic size, but are not impacted by production type. Another finding is that energy production covers only one-third of its costs. Also, both production volumes and costs were on a growth path on a year-over-year basis, with similar growth ratios. The European Union's leaders in energy consumption and production are the Netherlands, Germany, Belgium, Denmark, Czech Republic, Hungary, Luxembourg, Slovakia and Sweden. The study included the structuring of panel models for energy output and costs and identified their determinants. Energy output depends on total inputs and grows as they grow. Energy costs, in turn, are related to utilized agricultural area, total output and family farm income. An important limitation of this study is that FADN is a provider of high-level data. Hence, it is impossible to tell what specific sources of renewable energy are used by farms, and how they are affected by such exogenous factors as climate, earmarked subsidies or energy policy. The findings from this study are discussed in the context of the European Commission's recommendations laid down in the Bioeconomy Strategy of the EU (2013), the Seventh Environment Action Program, the New Innovation Agenda of the European Union, the Report "Transforming Our World: the United Nations 2030 Agenda for Sustainable Development" and the Circular Economy Action Plan. [ABSTRACT FROM AUTHOR]

This article examines the prospects of Scheffler solar receivers integrated into renewable energy power plants for civil applications. This kind of solar receiver can offer satisfactory energetic performance with acceptable energy conversion efficiency when compared to other technologies to harness solar energy since the high-quality focal receiver can reduce heat losses also supposing great levels of evaporation temperature. In this research, energetic optimization and a broad assessment of Scheffler-type solar receivers are thoroughly conducted for variable sun radiation and considering a broad range of working conditions. To achieve this goal, thermodynamic optimization of the chief factors was attained via a numerical model which calculated the energy efficiency of the Scheffler solar receiver at part-load working conditions by computing all energy losses negatively affecting the heat exchange phase in the cavity receiver. The results obtained in this study show that the solar collector efficiencies of Scheffler receivers appear more promising than that of usual parabolic trough collectors; moreover, Scheffler receivers persisted with less sensitivity to reductions in solar radiation intensity. For these reasons, solar power systems based on Scheffler-type systems can be used from tens to hundreds of kW to ensure the energetic supply of small urban settlements with acceptable efficiency, optimistic investments, simple construction and reduced overall sizes. [ABSTRACT FROM AUTHOR]

The text discusses the recent development and future perspectives of wind power generation, highlighting the rapid growth of wind energy globally. It covers topics such as wind potential assessment, wind power forecasting, wind power under climate change, socioeconomic and environmental factors of wind energy expansion, and wind turbine design development. The document emphasizes the need for improvements in research and the application of multidisciplinary approaches to address key challenges in wind energy utilization. The Special Issue 'Recent Development and Future Perspective of Wind Power Generation' provides valuable insights into these areas, closing some research gaps. [Extracted from the article]

Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from Thailand's biomass pellet production were comprehensively assessed, with a specific focus on wood and corn pellets. Employing the extended input and output tables, the anticipated economic and environmental effects of the rising demand for biomass pellets within the Asia–Pacific Economic Cooperation region, which is projected to see an increase exceeding 33% by the year 2050, were investigated. The estimations of CO2, CH4, and N2O emissions, which were conducted utilizing an open Leontief model based on the 2015 National Input–Output Tables, covered each stage of the production process. The results show that emissions from the production of corn pellets are expected to rise steadily, from 52.91 MtCO2e in 2022 to 75.77 MtCO2e by 2030, whereas emissions from wood pellet production are set to increase more substantially, from 210.30 to 301.18 MtCO2e within the same timeframe. Data derived from surveys and interviews with corn farmers and wood pellet manufacturers informed the lifecycle data for the biomass pellet supply chain from cradle to gate. The findings suggest that Thailand's power sector could benefit significantly from the biomass potential in the northern part of Thailand, which boasts an estimated energy content of corncob at 39 ktoe (0.0016 TJ). Market demand scenarios were explored in two forms: one where it was assumed that all biomass pellets are to be exported to Japan and South Korea, expecting a combined demand of approximately 560,262 tons by 2030, and another positing that 10% of production will be reserved for the domestic market, with a forecasted annual increase of 10% from 2020 to 2050. This paper highlights the need to prioritize low-emission renewable energy sources, expand technologies with lower lifecycle emissions, optimize the biomass supply chain to enhance efficiency, and introduce sustainable energy practices. The detailed GHG emissions analysis provides critical insights for policy formulation, underscoring the importance of sustainable transitions in the context of increasing biomass demand. [ABSTRACT FROM AUTHOR]

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research. [ABSTRACT FROM AUTHOR]

Under "dual-carbon" goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately. [ABSTRACT FROM AUTHOR]

The main aim of the research was to assess Polish rural municipalities' investment activity connected with the development of a low-carbon economy, supported with EU funds in the 2007–2013 and 2014–2020 financial frameworks. The empirical study was based on data from Poland's Ministry of Development Funds and Regional Policy, Ministry of Finance, and Statistics Poland, analysed through basic descriptive statistics and a logistic model to identify key factors influencing investment activity. The study showed that the greatest number of agreements on funding were, in fact, signed by rural municipalities in the period under analysis. The predominant measures undertaken during this time were the promotion of renewable energy sources and the improvement in energy efficiency. In the earlier financial framework, low-carbon economy projects in rural areas were more often implemented by municipalities with developing demographic potential, including those characterised by a higher level of enterprise development. In the subsequent financial framework, human capital turned out to be of key importance for the investment activity of rural municipalities. Experience gained from 2007–2013 positively influenced fund absorption in 2014–2020, improving project value, number, and support. The study confirmed that rural municipalities play a vital role in advancing a low-carbon economy, as local actions are key to achieving sustainable development and reducing greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Over the past few decades, wind energy has expanded to become a widespread, clean, and sustainable energy source. However, integrating offshore wind energy with the onshore AC grids presents many stability and control challenges that hinder the reliability and resilience of AC grids, particularly during faults. To address this issue, current grid codes require offshore wind farms (OWFs) to remain connected during and after faults. This requirement is challenging because, depending on the fault location and power flow direction, DC link over- or under-voltage can occur, potentially leading to the shutdown of converter stations. Therefore, this necessitates the proper understanding of key technical concepts associated with the integration of OWFs. To help fill the gap, this article performs an in-depth investigation of existing alternating current fault ride through (ACFRT) techniques of modular multilevel converter-based high-voltage direct current (MMC-HVDC) for OWFs. These techniques include the use of AC/DC choppers, flywheel energy storage devices (FESDs), power reduction strategies for OWFs, and energy optimization of the MMC. This article covers both scenarios of onshore and offshore AC faults. Given the importance of wind turbines (WTs) in transforming wind energy into mechanical energy, this article also presents an overview of four WT topologies. In addition, this article explores the advanced converter topologies employed in HVDC systems to transform three-phase AC voltages to DC voltages and vice versa at each terminal of the DC link. Finally, this article explores the key stability and control concepts, such as small signal stability and large disturbance stability, followed by future research trends in the development of converter topologies for HVDC transmission such as hybrid HVDC systems, which combine current source converters (CSCs) and voltage source converters (VSCs) and diode rectifier-based HVDC (DR-HVDC) systems. [ABSTRACT FROM AUTHOR]

Countries have turned to developing renewable energy production, avoiding the risks posed by the disruptions in global energy trade, the high volatility in energy prices, and the remarkable environmental impairment. Numerous economic, environmental, institutional, and social factors have been put forward as driving factors toward renewable energy. The goal of this research article is to study the causal nexus among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy of the 27 EU members between 2000 and 2020 through Emirmahmutoglu and Kose causality test. The results of the panel-level causality tests demonstrate feedback interplay among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy use. However, the results of the country-level causality analysis unveil that the interplay among renewable energy utilization, energy dependency, real GDP per capita, CO2 emissions, and human capital remarkably varies among EU members. The results of this study suggest that renewable energy investments are significant instruments to make progress in energy security, human capital, real GDP per capita, and CO2 emissions. Furthermore, energy security, human capital, real GDP per capita, and CO2 emissions are significant drivers of renewable energy development. [ABSTRACT FROM AUTHOR]

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided. [ABSTRACT FROM AUTHOR]

Emerging states' path to enhancing the welfare of their citizens has been strongly accompanied by environmental degradation; climate change effects often abrogate their economic results. This zero-sum game must change, and environmental concerns should be considered when the development of a country is discussed and assessed. In this sense, this study's objective is to analyze the impact of economic complexity and renewable energy consumption in the presence of economic growth and urbanization in selected emerging European countries from 1995 to 2021. We used a multiple-methodologic approach to highlight the supportive effects of economic complexity and renewable energy consumption in mitigating carbon emissions. Furthermore, the effects of economic growth and urbanization were emphasized by applying the cointegration regression (CCR), fully modified OLS, and dynamic OLS (FMOLS–DOLS) approaches. Additionally, we used Driscoll–Kraay estimation regression to test the robustness of our results. The results reveal the beneficial role of renewable energy consumption and economic complexity in the decarbonization process of selected countries. Furthermore, the study highlighted the detrimental influence of urbanization and economic growth, which were feasible considering the emerging status of the countries included in the panel. [ABSTRACT FROM AUTHOR]

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme. [ABSTRACT FROM AUTHOR]

Despite being resource-richly endowed with various energy sources, and despite the connection of 89.8% of the households to the grid in South Africa, the Eastern Cape province, as compared to other provinces, has the lowest level of grid connection of about 64.5%. Some of the rural poor households in the Eastern Cape province supplement their free basic electricity with unclean energy alternatives. Using unclean energy alternatives is not only detrimental to the environment and health of the people, but it is a sign of energy poverty and among the contributing factors to depesantization, deagrarianization, and deindustrialization which prolongs the underdevelopment in rural areas. Innovation in energy technologies is a key ingredient in meaningful rural development. The utilization of small-scale biomass gasification technologies can be a solution to the South African energy crisis in rural areas, and it is in line with sustainable development goal number 7, which is about ensuring access to affordable, reliable, sustainable, and modern energy for all. Alternative renewable energy sources cannot be ignored when dealing with the energy crises in South Africa. Renewable energy sources in the country include biomass, solar, wind, and hydropower. Despite its low utilization in the Eastern Cape province, small-scale biomass gasification technology remains pivotal in reducing energy crisis by producing electricity. However, the affordability of biomass gasification technology also plays a role in whether people will accept small-scale biomass gasification technology. The purpose of this paper is to determine the possibilities of using small-scale biomass gasification technology. This paper gives a comprehensive review of small-scale biomass gasification technology potential in the Eastern Cape province and the link between acceptance of small-scale gasification technology and affordability by evaluating the availability of biomass sources in the province and achievements with regards to small-scale biomass gasification. This paper also covers the impact of biomass gasification technology integration in the energy grid, what needs to be taken into consideration before its installation, its benefits and the barriers to its development in Eastern Cape province. [ABSTRACT FROM AUTHOR]

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties. [ABSTRACT FROM AUTHOR]

Rechargeable batteries play a crucial role in the utilization of renewable energy sources. Energy storage systems (ESSs) are designed to store renewable energy efficiently for immediate use. The market for energy storage systems heavily relies on lithium-ion batteries due to their high energy density, capacity, and competitiveness. However, the increasing cost and limited availability of lithium make long-term use challenging. As an alternative to Li-ion batteries, rechargeable seawater batteries are gaining attention due to their abundant and complementary sodium ion active materials. This study focuses on the preparation and characterization of Na3.0Zr2Si2PO12- and Na3.15Zr2Si2PO12-type ceramic membranes and testing their stability in seawater batteries used as solid electrolyte. From the surface analysis, it was observed that the Na3.15Zr2Si2PO12 powder showed a specific surface area of 2.94 m2/g compared to 2.69 m2/g for the Na3.0Zr2Si2PO12 powder. The measured NASICON samples achieved ionic conductivities between 7.42 × 10−5 and 4.4 × 10−4 S/cm compared to the NASICON commercial membrane with an ionic conductivity of 3.9 × 10−4 S/cm. Battery testing involved charging/discharging at various constant current values (0.6–2.0 mA), using Pt/C as the catalyst and seawater as the catholyte. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources (RESs) and power electronics devices has led to a continuous decline in power system stability. Due to the instability of grid-following converters (GFLCs) in weak grids, the grid-forming converters (GFMCs) have gained widespread attention featuring their flexible frequency and voltage regulation capabilities, as well as the satisfactory grid-supporting services, such as inertia and damping, et al. Notably, the risk of wideband oscillations in modern power grids is increasingly exacerbated by the reduced number of synchronous generators (SGs). Thus, the wideband oscillation suppression method based on adaptive active power droop control of GFMCs is presented in this paper. First, the stability of the hybrid grid-forming and grid-following system is obtained according to the improved short circuit ratio (ISCR), where the GFMC is in parallel at the point of common coupling (PCC) of the GFLC. Then, an adaptive adjustment strategy of the active power droop control is proposed to enhance the oscillation suppression capability across the full frequency range, thereby mitigating the wideband oscillation caused by phase-locked loop (PLL) synchronization in the GFLCs. Additionally, a first-order inertia control unit is added to the active and reactive power droop controllers to mitigate frequency and voltage variations as well as suppress potential mid-to-high frequency resonance. Finally, the wideband oscillation suppression strategy is validated by the simulation and experimental results. [ABSTRACT FROM AUTHOR]

This study presents a mathematical model for forecasting the development of Ukraine's Integrated Power System (IPS) until 2040, with a specific focus on the expansion of nuclear energy as a cornerstone of the nation's low-carbon transition. The model is an extension of Frank Bass's mixed influence diffusion model, incorporating both economic and technological factors. These factors are treated as stochastic variables essential for accurately predicting the evolution of an integrated energy system, particularly in the context of rapid renewable energy sources (RES) growth. The model employs regression techniques using generalized logistic curves, improving forecasting efficiency by aligning modeling parameters with experimental data. The study's results indicate the potential for optimizing IPS components, including nuclear and thermal power generation, through the model's application. The model is distinguished by its inclusion of economic and technological impacts, such as state matrices, control actions, and external influence matrices, which enhance the accuracy of simulations and predictions. The validation of the model, based on scenarios of electricity consumption and generation, shows significant alignment with observed trends, confirming the model's reliability. The findings suggest that this model is an effective tool for developing and refining energy system scenarios, with nuclear energy playing a pivotal role in Ukraine's sustainable energy future. [ABSTRACT FROM AUTHOR]

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs. [ABSTRACT FROM AUTHOR]

On-grid hybrid wind–solar systems are one of the best sustainable solutions for developing distributed generation, as they can provide a stable and reliable electricity supply, effectively using the potential of the two most common renewable energy resources. In Ukraine, promoting the development of on-grid hybrid wind–solar power plants takes on particular importance under conditions of electricity shortages caused by the large-scale destruction of the energy infrastructure due to the ongoing hostilities. This article examines the economic efficiency of installing such power plants in the residential sector of Ukraine under different state support schemes. This study was conducted for on-grid hybrid wind–solar systems of various configurations and installed capacities with different equity and debt capital proportions involved in implementing investment projects. This study's results highlight the economic efficiency of the feed-in tariff compared to the net billing for households investing in such facilities and emphasize the need to improve policy measures to increase their investment attractiveness. [ABSTRACT FROM AUTHOR]

Microgrids have emerged as a crucial focus in power engineering and sustainable energy research, with utility-scale microgrids playing a significant role in both developed and developing countries like the Philippines. This study presents a comprehensive framework for utility-scale microgrid planning, emphasizing the sustainable integration of renewable energy resources to the distribution grid. The framework addresses the operational modes of grid-connected and islanded microgrids, emphasizing the seamless transition between these modes to ensure a continuous power supply. By leveraging local distributed energy resources, the microgrid aims to reduce dependence on the main transmission grid while enhancing resilience and reliability. The proposed planning framework not only eases the economic burden of constructing renewable energy sources but also aids distribution utilities in maximizing local resources to achieve sustainable energy goals. Through a detailed network analysis and modeling, the framework provides a robust foundation for optimizing the energy mix and enhancing the overall system performance. This research contributes to advancing microgrid technology as a key driver towards achieving UN Sustainable Development Goals, particularly in promoting clean and affordable energy access. [ABSTRACT FROM AUTHOR]

When microgrids operate autonomously, they must curtail the surplus of renewable energy sources (RES) while minimising reliance on gas. However, when interconnected, microgrids can collaboratively minimise RES curtailment and gas consumption due to the ability of exchanging power. This paper presents a centralised controller and energy management of multiple standalone AC microgrids interconnected to a common AC bus using back-to-back converters. Each microgrid consists of RES, a battery, a gas-powered auxiliary unit, and a load. The battery's state of charge (SOC) is controlled and is used in the AC bus frequency to indicate whether the microgrid has a surplus or shortage of power. High-level global droop control exchanges power between the microgrids. The optimisation problem for this interconnected system is modelled cooperatively to determine the optimal dispatch solution that minimises the energy cost from the auxiliary unit. The optimal dispatch is solved in three cases using the Nelder–Mead simplex algorithm under different settings: one-variable optimisation, three-variable optimisation with the standard droop equation, and three-variable optimisation with a modified droop equation. The optimised performance results are compared with those of the non-optimised benchmark to determine the percentage of optimal performance. The simulation results show that the total energy cost from the auxiliary unit is minimised by 8.98%. [ABSTRACT FROM AUTHOR]

Residential and commercial buildings are responsible for 35% of the EU energy-related greenhouse gas (GHG) emissions. Reducing their emissions is crucial for meeting the challenging EU objective of the agenda for becoming a net-zero continent by 2050. The diffusion and integration of distributed renewable energy sources (RESs) and energy storage systems (ESSs), as well as the creation of energy communities (ECs), have proven to be crucial aspects in reducing GHG emissions. In this context, this article proposes a multi-agent AI-based control framework to solve the EC's energy management problem in the presence of distributed RESs and ESSs as well as considering a shared ESS. The objectives of the proposed control framework are to satisfy the EC members' load demand to maximize self-consumption and to manage ESSs charging and discharging processes, to enforce cooperative behavior among the EC members by adopting fair and personalized strategies and to maximize EC members' profits. The proposed control procedure is based on three sequential stages, each solved by a dedicated local RL agent exploiting the Q-Learning algorithm. To reduce the computational complexity of the proposed approach, specifically defined state aggregation criteria were defined to map the RL agents' continuous state spaces into discrete state spaces of limited dimensions. During the training phase, the EC members' profiles and the ESSs' and RESs' characteristics were randomly changed to allow the RL agents to learn the correct policy to follow in any given scenario. Simulations proved the effectiveness of the proposed approach for different costumers' load demand profiles and different EC configurations. Indeed, the trained RL agents proved to be able to satisfy the EC members' load demands to maximize self-consumption, to correctly use the distributed and shared ESSs, to charge them according to respective personalized criteria and to sell the energy surplus, prioritizing sales to the EC. The proposed control framework also proved to be a useful tool for understanding EC performance in different configurations and, thus, for properly dimensioning the EC elements. [ABSTRACT FROM AUTHOR]

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman's renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman's unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman's national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman's context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges. [ABSTRACT FROM AUTHOR]

The article mainly examines spatial diversification of the green economy in EU countries in 2014 and 2021 in the context of the energy transformation process. In the theoretical part of the work, the green economy concept, with reference to the conditions of the green energy, was analyzed. The research procedure used in the article is based on multidimensional comparative analysis. The empirical verification was conducted using green economy indicators that are published periodically by the OECD and Eurostat. Based on 21 indicators, a synthetic green economy index was designed for 27 EU member states. In the selected set of detailed indicators, those related to green energy economy played an important role. This approach allowed for the creation of rankings and comparisons between EU countries in 2014 and 2021, i.e., the implementation period of the Europe 2020 Strategy. In this period, the priority areas of EU development were: the low-carbon economy, including the use of renewable energy sources and improvement of energy efficiency, as well as the introduction of eco-innovation. Green energy should be the basis for the functioning of highly developed countries and socio-economic progress in the case of developing countries. Based on the analysis, a large discrepancy in terms of green economy was observed in the examined countries. Particular attention was paid to disproportions in the area of green energy. The average value of the synthetic measure of the green economy in the EU countries increased in the studied years from 0.4488 to 0.4529, which can be interpreted as a slight acceleration in the greening processes. The added value of the research presented in the paper and its novelty is the analysis of the current patterns of green transformation in EU member states, with particular emphasis on energy factors. [ABSTRACT FROM AUTHOR]

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks. [ABSTRACT FROM AUTHOR]

The article contains a description of the design solutions proposed by the authors for a hybrid wind turbine bearing, in which the sliding part takes over the load to the turbine shaft after reaching the shaft rotation speed, ensuring hydrodynamic lubrication of the plain bearing and relieving the rolling bearing. This allows for low starting resistance of the power plant and ensures quiet operation during use. Two conceptual solutions of a hybrid bearing were presented, differing in the shape of the plain bearing journal. A mechanism for automatic switching of the load between a rolling and a plain bearing was developed. A solid simulation model of this mechanism was built in the Autodesk Inventor—Dynamic Simulation software Inventor Professional 2023 environment, and its operation was simulated. The results confirmed the usefulness of using this design in shaft-bearing systems of wind turbines with a vertical axis of rotation. Based on the simulation, the speed at which the thrust roller bearing will be released was determined. Technical parameters of a plain bearing with a spherical journal shape were calculated. The height of the lubrication gap and the shaft rotational speed at which the bearing load capacity index reaches a critical value were determined. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the dynamic modeling of distribution power systems with a focus on the integration of renewable energy sources (RESs) for stability analysis. Our research delves into the static and dynamic behavior of distribution systems, emphasizing the need for enhanced load modeling to mitigate planning and operational uncertainties. Using MATLAB/Simulink®, we simulate four distinct study cases characterized by varying load types and levels of distributed generation (DG), particularly solar PV, under both balanced and unbalanced conditions. Our findings highlight the critical role of DG in influencing voltage stability, revealing that deviations in voltage and current during grid imbalances remain within acceptable limits. The study underscores the importance of DG-based inverters in maintaining grid stability through reactive power support and sets the stage for future research on microgrid simulations and battery storage integration to further enhance system stability and performance. [ABSTRACT FROM AUTHOR]

Research has been conducted on medium-voltage direct current (MVDC) to address the limited transmission capacity of existing AC power transmission lines and to achieve efficient integration of renewable energy sources. Another method to increase the transmission capacity is to raise the maximum allowable temperature of the power cable. The maximum allowable temperature for cross-linked polyethylene (XLPE) in commercial power cables is 90 °C. Polypropylene (PP) is considered as an alternative material. PP has a maximum allowable temperature of 110 °C and possesses thermoplastic properties, making it environmentally friendly. However, PP may not ensure uniformity of the insulation layer depending on the extrusion process, including cooling conditions. This study aimed to determine the applicability of MVDC cables by assessing the uniformity of the insulation layer of extruded cables, considering the cooling conditions of PP in specimens. For the cooling conditions, ambient air, cooling press, and water cooling were evaluated for DC breakdown strength. Furthermore, the uniformity of the breakdown strength of the insulation layer, which was divided into sections such as conductor and sheath, was evaluated for commercial PP, XLPE, and the developed PP cables. This study aims to provide a comprehensive analysis of the DC BD strength of PP under various cooling conditions and emphasize the importance of uniformity in extruded cable sections. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources, such as photovoltaic and wind systems, into energy grids has underscored the need for reliable control mechanisms to mitigate the inherent intermittency of these sources. According to the Brazilian grid operator (ONS), there have been cascading disconnections in renewable energy distributed systems (REDs) in recent years, highlighting the need for robust control models. This article addresses this issue by presenting the validation of an active power ramp rate control (PRRC) function for a PV plant coupled with a Battery Energy Storage System (BESS) using WECC generic models. The proposed model underwent rigorous validation over an extended analysis period, demonstrating good accuracy using the Root Mean Squared Error (RMSE) and an R-squared (R2) metrics for the active power injected at the Point of Connection (POI), PV active power, and BESS State of Charge (SOC), providing valuable insights for medium and long-term analyses. The ramp rate control module was implemented within the plant power controller (PPC), leveraging second-generation Renewable Energy Systems (RES) models developed by the Western Electricity Coordination Council (WECC) as a foundational framework. We conducted simulations using the Anatem software, comparing the results with real-world data collected at 100 ms to 1000 ms intervals from a PV plant equipped with a BESS in Brazil. The proposed model underwent rigorous validation over an extended analysis period, with the presented results based on two days of measurements. The positive sequence model used to represent this control demonstrated good accuracy, as confirmed by metrics such as the Root Mean Squared Error (RMSE) and R-squared (R2). Furthermore, the article underscores the critical role of accurately accounting for the power sampling rate when calculating the ramp rate. [ABSTRACT FROM AUTHOR]

In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making. [ABSTRACT FROM AUTHOR]

An AC (alternative current) power integration of distributed energies faces multi-fold challenges such as synchronization and weak grid-induced instability. In this study, a multi-terminal DC transformer is proposed for renewable energy clusters grid connection. The DC transformer provides multiple DC input ports for renewable energy collection, while the load port is connected to the medium-voltage DC grid via a modular multilevel converter (MMC). The multi-port topology enables flexible power transfer between multiple input sources to the load without additional components. The carrier layer modulation strategy is implemented to balance the MMC module voltage; bidirectional power transmission between multiple input sources is achieved through the phase shift modulation (PSM) method. First, we provided a detailed introduction of the proposed topology and working principle. A simulation model was built using the SIMULINK, and the simulation results verified the effectiveness of the proposed converter modulation strategy and phase shift modulation method. A corresponding hardware experimental platform was designed and built, and the modulation and voltage equalization functions of modular multilevel rectifiers were presented as well as the measured results of power transmission modulation of the converter under single-input and multi-input conditions. The results indicate that the proposed transformer can achieve multiple DC inputs and has multi-channel power transmission capabilities, making it suitable for renewable energy clusters grid connection. [ABSTRACT FROM AUTHOR]

To combat global warming, energy systems are transitioning to generation from renewable sources, such as wind and solar, which are sensitive to climate conditions. While their output is expected to be little affected by global warming, wind, and solar electricity generation could be affected by more drastic climatic changes, such as abrupt sunlight reduction scenarios (ASRSs) caused by nuclear war ("nuclear winter") or supervolcanic eruptions ("volcanic winter"). This paper assesses the impacts of an ASRS on global energy supply and security in a 100% renewable energy scenario. National generation mixes are determined according to roadmaps for a global transition to renewable energy, with wind and solar contributing a combined 94% of the global energy supply. Wind and solar generation are determined for a baseline climate and an ASRS following a large-scale nuclear exchange. While effects vary by country, overall wind and solar generation are expected to reduce by 59% in the first year following an ASRS, requiring over a decade for full recovery. Ensuring sufficient energy for everyone's critical needs, including water, food, and building heating/cooling, would require international trade, resilient food production, and/or resilient energy sources, such as wood, geothermal, nuclear power, tidal power, and hydropower. [ABSTRACT FROM AUTHOR]

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential. [ABSTRACT FROM AUTHOR]

The study aims to conduct a comparative analysis of policies governing the expansion of renewable energy in Mongolia and selected countries. Against the backdrop of global energy transitions and Mongolia's recent energy challenges, this research aims to identify and evaluate policy frameworks that facilitate the sustainable growth of renewable energy sources. The study delves into the unique socio-economic and geopolitical context of Mongolia, emphasizing the nation's energy dependence on Russia. The findings of this comparative analysis provide valuable insights for Mongolian policymakers, offering recommendations for enhancing domestic policies that encourage the diversification of energy sources and attract foreign investment. By drawing on successful practices from some countries, this paper aims to contribute to the formulation of effective and context-specific strategies for Mongolia to achieve a more sustainable and resilient energy landscape. [ABSTRACT FROM AUTHOR]

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5 % . [ABSTRACT FROM AUTHOR]

Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system's behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC's safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude–Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinear model (NLMPC) incorporating exact Froude–Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinear model also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC. [ABSTRACT FROM AUTHOR]

Robust control mechanisms are needed in microgrids to ensure voltage source inverters (VSIs) effectively integrate renewable energy sources such as solar photovoltaic (PV) systems into the power network. Current control approaches often have limitations regarding velocity, stability, and robustness. The paper details a newly developed method named Point of Common Coupling Direct Power Control (PCC-DPC) for renewable energy systems connected to the grid. PCC-DPC is used to instantly control voltage at the point of common coupling (PCC) inside the microgrid as opposed to other conventional techniques. This leads to a simplified controller design that does not require complex Park transformations and phase-locked loop (PLL) systems, and has a lower computational burden and less power fluctuation in a stable manner. Moreover, this research critically examines power quality phenomena through comparing PCC-DPC with a Vector Current Controller (VCC). Simulations performed on an Opal Re-al-Time simulator showed improved tracking performance and overall system efficiency due to the PCC-DPC approach over others. These results demonstrate that it can effectively be used as one of the most suitable methods for integrating renewable energy into electricity grids, which is reliable in regards to changes in power grid dynamics. [ABSTRACT FROM AUTHOR]